Leveraging Contact Network Information in Clustered Randomized Studies of Contagion Processes.

In a randomized study, leveraging covariates related to the outcome (e.g. disease status) may produce less variable estimates of the effect of exposure. For contagion processes operating on a contact network, transmission can only occur through ties that connect affected and unaffected individuals; the outcome of such a process is known to depend intimately on the structure of the network. In this paper, we investigate the use of contact network features as efficiency covariates in exposure effect estimation. Using augmented generalized estimating equations (GEE), we estimate how gains in efficiency depend on the network structure and spread of the contagious agent or behavior. We apply this approach to simulated randomized trials using a stochastic compartmental contagion model on a collection of model-based contact networks and compare the bias, power, and variance of the estimated exposure effects using an assortment of network covariate adjustment strategies. We also demonstrate the use of network-augmented GEEs on a clustered randomized trial evaluating the effects of wastewater monitoring on COVID-19 cases in residential buildings at the the University of California San Diego.

Network methods and design of randomized trials: Application to investigation of COVID-19 vaccination boosters.

Network science methods can be useful in design, monitoring, and analysis of randomized trials for control of spread of infections. Their usefulness arises from the role of statistical network models in molecular epidemiology and in study design. Computational models, such as agent-based models that propagate disease on simulated contact networks, can be used to investigate the properties of different study designs and analysis plans. Particularly valuable is the use of these methods to assess how magnitude and detectability of intervention effects depend on both individual-level and network-level characteristics of the enrolled populations. Such investigation also provides an important approach to assessing consequences of study data being incomplete or measured with error. To address these goals, we consider two statistical network models: exponential random graph models and the more flexible congruence class models. We focus first on an historical use of these methods in design and monitoring of a cluster randomized trial in Botswana to evaluate the effect of combination HIV prevention modalities compared to standard of care on HIV incidence. We then present a framework for the design of a study of booster vaccine effects on infection with, and forward transmission of, SARS-CoV-2 variants. Motivation for the study is driven in part by guidance from the United Kingdom to base approval of booster vaccines with "strain changes" that target variants on results of neutralizing antibody tests and information about safety, but without requiring evidence of clinical efficacy. Using designs informed by our agent-based network models, we show it may be feasible to conduct a trial of novel SARS-CoV-2 vaccines in a single large campus to obtain useful information regarding vaccine efficacy against susceptibility and infectiousness. If needed, the sample size could be increased by extending the study to a small number of campuses. Novel network methods may be useful in developing pragmatic SARS-CoV-2 vaccine trials that can leverage existing infrastructure to reduce costs and hasten the development of results.

The vitamin D for COVID-19 (VIVID) trial: A pragmatic cluster-randomized design.

OBJECTIVES: To determine the effect of vitamin D supplementation on disease progression and post-exposure prophylaxis for COVID-19 infection. We hypothesize that high-dose vitamin D3 supplementation will reduce risk of hospitalization/death among those with recently diagnosed COVID-19 infection and will reduce risk of COVID-19 infection among their close household contacts. METHODS: We report the rationale and design of a planned pragmatic, cluster randomized, double-blinded trial (N = 2700 in total nationwide), with 1500 newly diagnosed individuals with COVID-19 infection, together with up to one close household contact each (~1200 contacts), randomized to either vitamin D3 (loading dose, then 3200 IU/day) or placebo in a 1:1 ratio and a household cluster design. The study duration is 4 weeks. The primary outcome for newly diagnosed individuals is the occurrence of hospitalization and/or mortality. Key secondary outcomes include symptom severity scores among cases and changes in the infection (seroconversion) status for their close household contacts. Changes in vitamin D 25(OH)D levels will be assessed and their relation to study outcomes will be explored. CONCLUSIONS: The proposed pragmatic trial will allow parallel testing of vitamin D3 supplementation for early treatment and post-exposure prophylaxis of COVID-19. The household cluster design provides a cost-efficient approach to testing an intervention for reducing rates of hospitalization and/or mortality in newly diagnosed cases and preventing infection among their close household contacts.